We use BERT [1]} for multi-task learning. We jointly learn the intent of the search query (store lookup) and also the named entities (store locations). We find that this method performs better in terms of precision (which is more important than recall). The false positive rate improved significantly through the global query intent loss function combined with a tagging loss function. As far as we know, this is the first attempt to jointly learn task specific NER along with a text classification loss function. We found one other piece of work that does something similar using LSTM networks [2]}. A good overview of multi-task learning can be found in [3]} and [4]}. This page does something very similar using BERT but its not completely clear on whether their work uses multi-task learning.
